Environmental factors interact with gene variants to influence patterns of alcohol use, abuse, adaptation, addiction, withdrawal, and relapse. Ethanol is a highly soluble small molecule that has subtle allosteric effects on many molecular processes in the CNS and not all of its many targets are known. Individual differences in how humans respond to ethanol are also not well understood. This project leverages the broad expertise of INIA to explore and test genetic, molecular, synaptic, cellular, and behavioral causes of alcohol consumption and the increased vulnerability following stressors. Our focus is on exploiting new high-resolution genomic resources (sequence data, SNPs, mRNA microarrays) to model networks of molecular and synaptic interactions in forebrain regions that have important roles in alcoholism. To ensure robust results we use two large genetic reference populations of rodents (BXD mice and HXB rats) and apply sophisticated statistical methods to generate hypotheses that are then subjected to rigorous experimental testing. We combine data from our own work with data from numerous other published studies using a systems genetics approach. This combined approach is made possible by our use of well studied genetic reference populations. We will define shared and unique genetic and synaptic factors that modulate ethanol use and the convergent effects of stress on ethanol addiction and relapse. In Aim 1 (Data Generation) we generate normative expression data and networks for four key forebrain regions (medial prefrontal cortex, nucleus accumbens, bed nucleus of the stria terminalis, and the basolateral amygdala) from complementary genetic reference populations with different genetic structures (inbred Rl and hybrid RIX lines). We are acquiring gene expression data for 48 brain regions in C57BU6J and DBA/2J lines. Our goal is to extract robust networks that are cross-validated and that have strong prospects of generalizing to admixed human populations. Data will be made publicly available on the GeneNetwork (GN) site (www.genenetwork.org). In Aim 2 (Model Construction) we develop open source programs and standard operating procedures to produce well defined and testable hypotheses. The INIA Models Work Group will be responsible for developing, testing, and using the INIA GeneNetwork (GN) and this software to construct explicit process diagrams and statistical models. We will integrate these models into GN for use and critique by INIA and NIAAA researchers. In Aim 3 (Predictive Validation), members of INIA will experimentally manipulate sets of isogenic lines (BXD and RIX), predicting whether they will be high or low responders using INIA standard operating protocols. The synergy among genetic, transcriptome, electrophysiological, and experimental studies and data sets will allow us to test the role of complex interactions in the mesocorticolimbic system that contribute to alcoholism and maladaptive stress response.